Image segmentation using k-means clustering — image_segment

Segments image objects using clustering by the k-means clustering algorithm

Usage

image_segment_kmeans(
  img,
  bands = 1:3,
  nclasses = 2,
  invert = FALSE,
  filter = FALSE,
  fill_hull = FALSE,
  plot = TRUE
)

Arguments

img: An Image object.
bands: A numeric integer/vector indicating the RGB band used in the segmentation. Defaults to 1:3, i.e., all the RGB bands are used.
nclasses: The number of desired classes after image segmentation.
invert: Invert the segmentation? Defaults to FALSE. If TRUE the binary matrix is inverted.
filter: Applies a median filtering in the binary matrix? Defaults to FALSE. Use a numeric integer to indicate the size of the median filter.
fill_hull: Fill holes in the objects? Defaults to FALSE.
plot: Plot the segmented image?

Value

A list with the following values:

image The segmented image considering only two classes (foreground and background)
clusters The class of each pixel. For example, if ncluster = 3, clusters will be a two-way matrix with values ranging from 1 to 3. masks A list with the binary matrices showing the segmentation.

References

Hartigan, J. A. and Wong, M. A. (1979). Algorithm AS 136: A K-means clustering algorithm. Applied Statistics, 28, 100–108. doi:10.2307/2346830

Examples

img <- image_pliman("la_leaves.jpg", plot = TRUE)

seg <- image_segment_kmeans(img)

seg <- image_segment_kmeans(img, fill_hull = TRUE, invert = TRUE, filter = 10)